A new method called maximally correlated PCA based on deep parameterization learning (MCPCADP) has been developed to reduce the dimensions of high-dimensional data by considering nonlinear correlations. This model maximizes the Ky-Fan norm of the covariance matrix of nonlinearly mapped data features. The researchers tested the method on synthetic and real-world databases and found that it performs similarly to other popular algorithms.